Joint Manifolds for Data Fusion

12 years 9 months ago
Joint Manifolds for Data Fusion
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of cameras can generate massive amounts of high-dimensional image and video data. One way to cope with such a data deluge is to develop low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a low-dimensional set of parameters, as is often the case in a sensor network. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for a vari...
Mark A. Davenport, Chinmay Hegde, Marco F. Duarte,
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIP
Authors Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk
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